The electrical activity of the heart is presented by the surface electrocardiogram (ECG) signal. Due to ease of use and non-invasiveness, ECG is not only used as a prime tool to monitor the functionality of the heart but also to diagnose cardiac arrhythmia by extracting information about intervals, amplitudes, and wave morphologies of the different P, QRS, and T waves. The extracted features from the ECG signal play an essential role in diagnosing many cardiac diseases. Hence, the development of real-time and accurate delineation methods is crucial, especially for abnormal ECG signals. Two main components of blocks can be classified, which are QRS detection and wave delineation.
A QRS complex, which is a principal component in the cardiac cycle, is used as a reference and represents the depolarization of ventricles in the heart. This amplitude rises to 1 mV or 2 mV above or below the isoelectric line for normal heartbeats and can be several times larger for abnormal heartbeats. The time required for the ventricles to depolarize defines the QRS width or interval and typically lasts between 80 ms and 120 ms.
In ECG signal analysis, accurate location of the position of the QRS complex is known as QRS detection and is a key to automatic techniques. The amplitude of the QRS complex is larger compared to the amplitude of the other waves that make up the ECG signal. As a result, detection processes for the QRS complex are easier in comparison. Various signal processing of QRS detection techniques have been proposed in literature. Time domain thresholding along with filtering such as first derivative, second derivative, both derivatives, and matched filter are some of the earliest techniques that are suitable for real-time implementation. Other methods that provide enhanced accuracy are based on spectral analysis of the ECG signal. For example, the wavelet transform is a tool to analyze ECG signals. As part of the spectral analysis techniques, the discrete Fourier transform has been reported in the literature to detect the QRS complex. Empirical mode decomposition and the Hilbert transform have been introduced to improve the analysis of the QRS detection of nonlinear and nonstationary ECG signals. Moreover, principal component analysis (PCA) that literally transforms the ECG data into a new coordinate system has been proposed in related art. QRS complex detection techniques could also be used with the concept of machine learning, classification, and pattern recognition. These QRS complex detection techniques are generally applicable when the QRS complex is used in the diagnosis of cardiac arrhythmia. QRS complex detection techniques include fuzzy logic, artificial neural network, neuro-fuzzy networks, support vector machine, and combinations of such techniques.
Delineation, which is the stage where fiducial points and the limits of the ECG waves are determined, is essential to the extraction of ECG parameters such as the ST interval and the QT interval. The localization of wave peaks is easier to detect than the onsets and offsets, as the signal-to-noise ratio is higher and becomes lower at the wave boundaries where the noise level dominates the ECG signal, which in turn leads to a complex delineation process. Generally, ECG wave delineation is performed after detecting the QRS complex where a set of search windows is defined to locate the T and the P wave. The search window enhances the characteristics of the targeted waves using different approaches proposed in related art literature.
A delineation technique based on the time curve derivative of digital signals is proposed in the related art. Adaptive filters and their different forms have also been used in the ECG delineation process. Time domain morphology and gradient, hidden Markov models, and Bayesian approaches along with the Gibbs sampler are other methods that offer a wide range of complexity, flexibility, accuracy, and cost. However, none of these related art delineation techniques are completely self-adaptive when performed by a medical device for the purpose of automated ECG feature extraction. Thus, there is a need for a medical device that provides self-adaptive automated ECG feature extraction.